Refining predictive models in critically ill patients with acute renal failure JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY Mehta, R. L., Pascual, M. T., Gruta, C. G., Zhuang, S. P., Chertow, G. M. 2002; 13 (5)


Mortality rates in acute renal failure remain extremely high, and risk-adjustment tools are needed for quality improvement initiatives and design (stratification) and analysis of clinical trials. A total of 605 patients with acute renal failure in the intensive care unit during 1989-1995 were evaluated, and demographic, historical, laboratory, and physiologic variables were linked with in-hospital death rates using multivariable logistic regression. Three hundred and fourteen (51.9%) patients died in-hospital. The following variables were significantly associated with in-hospital death: age (odds ratio [OR], 1.02 per yr), male gender (OR, 2.36), respiratory (OR, 2.62), liver (OR, 3.06), and hematologic failure (OR, 3.40), creatinine (OR, 0.71 per mg/dl), blood urea nitrogen (OR, 1.02 per mg/dl), log urine output (OR, 0.64 per log ml/d), and heart rate (OR, 1.01 per beat/min). The area under the receiver operating characteristic curve was 0.83, indicating good model discrimination. The model was superior in all performance metrics to six generic and four acute renal failure-specific predictive models. A disease-specific severity of illness equation was developed using routinely available and specific clinical variables. Cross-validation of the model and additional bedside experience will be needed before it can be effectively applied across centers, particularly in the context of clinical trials.

View details for DOI 10.1097/01.ASN.0000014692.19351.52

View details for Web of Science ID 000175210800025

View details for PubMedID 11961023